1. Field of the Invention
The invention relates generally to the field of computerized, automated assessment of medical images, and more particularly to methods, systems, and computer program products for computer-aided detection and computer-aided detection of abnormalities (such as lesions and lung nodules) in medical images (such as low-dose CT scans) using artificial intelligence techniques (such as artificial neural networks, ANNs).
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348; 6,240,201; 6,282,305; 6,282,307; 6,317,617;
as well as U.S. patent applications Ser. Nos. 08/173,935; 08/398,307 (PCT Publication WO 96/27846); Ser. Nos. 08/536,149; 08/900,189; 09/027,468; 09/141,535; 09/471,088; 09/692,218; 09/716,335; 09/759,333; 09/760,854; 09/773,636; 09/816,217; 09/830,562; 09/818,831; 09/842,860; 09/860,574; 60/160,790; 60/176,304; 60/329,322; 09/990,311; 09/990,310; 60/332,005; 60/331,995; and 60/354,523;
as well as co-pending U.S. patent applications (listed by attorney docket number) 215752US-730-730-20, 216439US-730-730-20, 218013US-730-730-20, and 218221US-730-730-20;
as well as PCT patent applications PCT/US98/15165; PCT/US98/24933; PCT/US99/03287; PCT/US00/41299; PCT/US01/00680; PCT/US01/01478 and PCT/US01/01479,
all of which documents are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as those described in the documents identified in the following List of References that are cited throughout the specification:
1. R. T. Greenlee, M. B. Hill-Harmon, T. Murray, and M. Thun, xe2x80x9cCancer statistics, 2001,xe2x80x9d CA A Cancer Journal for Clinicans 51, 15-36 (2001).
2. R. T. Heelan, B. J. Flehinger, M. R. Melamed, M. B. Zaman, W. B. Perchick, J. F. Caravelli, and N. Martini, xe2x80x9cNon-small-cell lung cancer: Results of the New York screening program,xe2x80x9d Radiology 151, 289-293 (1984).
3. S. Sone et al., xe2x80x9cMass screening for lung cancer with mobile spiral computed topography scanner,xe2x80x9d The Lancet 351, 1242-124 (1998).
4. M. Kaneko, K. Eguchi, H. Ohmatsu, R. Kakinuma, T. Naruke, K. Suemasu, and N. Moriyama, xe2x80x9cPeripheral lung cancer: Screening and detection with low-dose spiral CT versus radiography,xe2x80x9d Radiology 201, 798-802 (1996).
5. C. I. Henschke et al., xe2x80x9cEarly Lung Cancer Action Project: Overall design and findings from baseline screening,xe2x80x9d The Lancet 354, 99-105 (1999).
6. J. W. Gurney, xe2x80x9cMissed lung cancer at CT: Imaging findings in nine patients,xe2x80x9d Radiology 199, 117-122 (1996).
7. F. Li, S. Sone, H. Abe, H. MacMahon, S. G. Armato III, and K. Doi, xe2x80x9cMissed lung cancers in low-dose helical CT screening program obtained from a general population,xe2x80x9d (submitted to Radiology 2002).
8. S. Yamamoto, I. Tanaka, M. Senda, Y. Tateno, T. Iinuma, T. Matsumoto, and M. Matsumoto, xe2x80x9cImage processing for computer-aided diagnosis of lung cancer by CT (LDCT),xe2x80x9d Systems and Computers in Japan 25, 67-80 (1994).
9. T. Okumura, T. Miwa, J. Kako, S. Yamamoto, M. Matsumoto, Y. Tateno, T. Iinuma, and T. Matsumoto, xe2x80x9cImage processing for computer-aided diagnosis of lung cancer screening system by CT (LDCT),xe2x80x9d In Proc. SPIE, 3338, 1314-1322 (1998).
10. W. J. Ryan, J. E. Reed, S. J. Swensen, and J. P. F. Sheedy, xe2x80x9cAutomatic detection of pulmonary nodules in CT,xe2x80x9d In Proc. Computer Assisted Radiology, pp. 385-389 (1996).
11. K. Kanazawa, M. Kubo, N. Niki, H. Satoh, H. Ohmatsu, K. Eguchi, and N. Moriyama, xe2x80x9cComputer assisted lung cancer diagnosis based on helical images,xe2x80x9d In Image Analysis Applications and Computer Graphics: Proc. Int. Computer Science Conf., pp. 323-330 (1995).
12. M. L. Giger, K. T. Bae, and H. MacMahon, xe2x80x9cComputerized detection of pulmonary nodules in computed tomography images,xe2x80x9d Investigative Radiology 29, 459-465 (1994).
13. S. G. Armato III, M. L. Giger, J. T. Blackbur, K. Doi, and H. MacMahon, xe2x80x9cThree-dimensional approach to lung nodule detection in helical CT,xe2x80x9d In Proc. SPIE, 3661, 553-559 (1999).
14. S. G. Armato III, M. L. Giger, C. J. Moran, J. T. Blackbur, K. Doi, and H. MacMahon, xe2x80x9cComputerized detection of pulmonary nodules on CT scans,xe2x80x9d Radiographics 19, 1303-1311 (1999).
15. S. G. Armato III, M. L. Giger, and H. MacMahon, xe2x80x9cAnalysis of a three-dimensional lung nodule detection method for thoracic CT scans,xe2x80x9d In Proc. SPIE, 3979, 103-109 (2000).
16. S. G. Armato III, M. L. Giger, and H. MacMahon, xe2x80x9cAutomated detection of lung nodules in CT scans: Preliminary results,xe2x80x9d Medical Physics 28, 1552-1561 (2001).
17. J. P. Ko and M. Betke, xe2x80x9cAutomated nodule detection and assessment of change over time-preliminary experience,xe2x80x9d Radiology 218, 267-273 (2001).
18. S. Sone, F. Li, Z.-G. Yang, S. Takashima, Y. Maruyama, M. Hasagawa, J.-C. Wang, S. Kawakami, and T. Honda, xe2x80x9cResults of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner,xe2x80x9d British Journal of Cancer 84, 25-32 (2001).
19. S. R. Sternberg, xe2x80x9cGrayscale morphology,xe2x80x9d Computer Vision, Graphics, and Image Processing 35, 333-355 (1986).
20. J. H. M. Austin, N. L. Muller, P. J. Friedman, D. M. Hansell, D. P. Naidich, M. Remy-Jardin, W. R. Webb, and E. A. Zerhouni, xe2x80x9cGlossary of terms for CT of the lungs: Recommendations of the nomenclature committee of the Fleischner Society,xe2x80x9d Radiology 200, 327-331 (1996).
21. S. G. Armato III, F. Li, M. L. Giger, H. MacMahon, S. Sone, and K. Doi, xe2x80x9cPerformance of automated CT nodule detection on missed cancers from a lung cancer screening program,xe2x80x9d (submitted to Radiology 2002).
22. K. Arakawa and H. Harashima, xe2x80x9cA nonlinear digital filter using mufti-layered neural networks,xe2x80x9d In Proc. IEEE Int. Conf. Communications, 2, 424-428 (1990).
23. L. Yin, J. Astola, and Y. Neuvo, xe2x80x9cA new class of nonlinear filtersxe2x80x94neural filters,xe2x80x9d IEEE Trans. Signal Processing 41, 1201-1222 (1993).
24. L. Yin, J. Astola, and Y. Neuvo, xe2x80x9cAdaptive multistage weighted order statistic filters based on the back propagation algorithm,xe2x80x9d IEEE Trans. Signal Processing 42, 419-422 (1994).
25. H. Hanek and N. Ansari, xe2x80x9cSpeeding up the generalized adaptive neural filters,xe2x80x9d IEEE Trans. Image Processing 5, 705-712 (1996).
26. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, xe2x80x9cA recurrent neural filter for reducing noise in medical X-ray image sequences,xe2x80x9d In Proc. Int. Conf. Neural Information Processing, 1, 157-160 (1998).
27. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, xe2x80x9cNoise reduction of medical X-ray image sequences using a neural filter with spatiotemporal inputs,xe2x80x9d In Proc. Int. Symp. Noise Reduction for Imaging and Communication Systems, pp. 85-90 (1998).
28. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cTraining under achievement quotient criterion,xe2x80x9d In Neural Networks for Signal Processing X, pp. 537-546 (IEEE Press, Piscataway, N.J., 2000).
29. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cSignal-preserving training for neural networks for signal processing,xe2x80x9d In Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems, 1, 292-297 (2000).
30. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cNeural filter with selection of input features and its application to image quality improvement of medical image sequences,xe2x80x9d In Proc. IEEE Int. Symp. Intelligent Signal Processing and Communication Systems, 2, 783-788 (2000).
31. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cEfficient approximation of a neural filter for quantum noise removal in X-ray images,xe2x80x9d (to be published in) IEEE Trans. Signal Processing 50 (2002).
32. I. Horiba, K. Suzuki, and T. Hayashi, xe2x80x9cImage processing apparatus for performing image converting process by neural network,xe2x80x9d U.S. Pat. No. 6,084,981 (filed in 1996).
33. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cEdge detection from noisy images using a neural edge detector,xe2x80x9d In Neural Networks for Signal Processing X, pp. 487-496 (IEEE Press, Piscataway, N.J., 2000).
34. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cNeural edge detector -a good mimic of conventional one yet robuster against noise-,xe2x80x9d Lecture Notes in Computer Science, Bio-Inspired Applications of Connectionism 2085, 303-310 (2001).
35. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, xe2x80x9cExtraction of the contours of left ventricular cavity, according with those traced by medical doctors, from left ventriculograms using a neural edge detector,xe2x80x9d In Proc. SPIE, 4322, 1284-1295 (2001).
36. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, xe2x80x9cContour extraction of the left ventricular cavity from digital subtraction angiograms using a neural edge detector,xe2x80x9d (to be published in) Systems and Computers in Japan 33 (2002).
37. K. Suzuki, I. Horiba, K. Ikegaya, and M. Nanki, xe2x80x9cRecognition of coronary arterial stenosis using neural network on DSA system,xe2x80x9d Systems and Computers in Japan 26, 66-74 (1995).
38. K. Suzuki, I. Horiba, N. Sugie, and M. Nanki, xe2x80x9cComputer-aided diagnosis system for coronary artery stenosis using a neural network,xe2x80x9d In Proc. SPIE, 4322, 1771-1782 (2001).
39. K. Funahashi, xe2x80x9cOn the approximate realization of continuous mappings by neural networks,xe2x80x9d Neural Networks 2, 183-192 (1989).
40. A. R. Barron, xe2x80x9cUniversal approximation bounds for superpositions of a sigmoidal function,xe2x80x9d IEEE Trans. Information Theory 39, 930-945 (1993).
41. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, xe2x80x9cLearning representations of back-propagation errors,xe2x80x9d Nature 323, 533-536 (1986).
42. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, in Learning internal representations by error propagation, Vol. 1 of Parallel Distributed Processing (MIT Press, MA, 1986), Chap. 8, pp. 318-362.
43. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cDesigning the optimal structure of a neural Filter,xe2x80x9d In Neural Networks for Signal Processing VIII, pp. 323-332 (IEEE Press, Piscataway, N.J., 1998).
44. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cA simple neural network pruning algorithm with application to filter synthesis,xe2x80x9d Neural Processing Letters 13, 43-53 (2001).
45. K. Suzuki, I. Horiba, and N. Sugie, xe2x80x9cSimple unit-pruning with gain-changing training,xe2x80x9d In Neural Networks for Signal Processing XI, pp. 153-162 (IEEE Press, Piscataway, N.J., 2001).
46. D. P. Chakraborty and L. H. L. Winter, xe2x80x9cFree-response methodology: Alternate analysis and a new observer-performance experiment,xe2x80x9d Radiology 174, 873-881 (1990).
47. C. E. Metz, xe2x80x9cROC methodology in radiologic imaging,xe2x80x9d Invest. Radiology 21, 720-733 (1986).
48. C. E. Metz, B. A. Herman, and J.-H. Shen, xe2x80x9cMaximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data,xe2x80x9d Stat. Med. 17, 1033-1053 (1998).
49. J. A. Hanley and B. J. McNeil, xe2x80x9cA method of comparing the areas under receiver operating characteristic curves derived from the same cases,xe2x80x9d Radiology 148, 839-843 (1983).
50. S. Haykin, Neural Networksxe2x80x94a comprehensive foundation, 2nd ed. (Prentice-Hall, Upper Saddle River, N.J., 1999).
51. W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, xe2x80x9cComputerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,xe2x80x9d Medical Physics 21, 517-524 (1994).
52. H.-P. Chan, S.-C. B. Lo, and B. Sahiner, xe2x80x9cComputer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network,xe2x80x9d Medical Physics 22, 1555-1567 (1995).
53. S.-C. B. Lo, H.-P. Chan, J.-S. Lin, H. Li, M. T. Freedman, and S. K. Mun, xe2x80x9cArtificial convolution neural network for medical image pattern recognition,xe2x80x9d Neural Networks 8, 1201-1214 (1995).
54. W. Zhang, K. Doi, M. L. Giger, R. M. Nishikawa, and R. A. Schmidt, xe2x80x9cAn improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms,xe2x80x9d Medical Physics 23, 595-601 (1996).
55. B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, xe2x80x9cClassification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images,xe2x80x9d IEEE Trans. on Medical Imaging 15, 598-610 (1996).
56. M. N. Gurcan, B. Sahiner, H.-P. Chan, L. Hadjiiski, and N. Petrick, xe2x80x9cSelection of an optimal neural network architecture for computer-aided detection of microcalcifications-Comparison of automated optimization techniques,xe2x80x9d Medical Physics 28, 1937-1948 (2001).
The contents of each of these references, including patents and patent applications, are incorporated herein by reference. The techniques disclosed in the patents, patent applications and other references can be utilized as part of the present invention.
Lung cancer continues to rank as the leading cause of cancer death among Americans and has expected to cause 157,400 deaths in the United States in 2001 (Ref. 1). Some evidence suggests that early detection of lung cancer may allow more timely therapeutic intervention and thus a more favorable prognosis for the patient (Refs. 2, 3). The sensitivity of helical computed tomography (CT) for lung nodule detection is significantly superior to that of conventional CT. Accordingly, screening programs for lung cancer with low-dose helical CT have been carried out in the United States and Japan (Refs. 4, 5). With helical CT, a number of CT images are acquired during a single CT examination.
Radiologists have to read many CT images. This may lead to xe2x80x9cinformation overloadxe2x80x9d for the radiologists. Furthermore, radiologists may miss many cancers during interpretation of CT images in a lung cancer screenings (Refs. 6, 7). Therefore, a computer-aided diagnosis (CAD) scheme for detection of lung nodules in low-dose CT images has been investigated as a useful tool for lung cancer screening.
Many investigators have developed a number of methods for the automated detection of lung nodules in CT scans, based on morphological filtering (Refs. 8, 9), geometric modeling (Ref. 10), fuzzy clustering (Ref. 11), and gray-level thresholding (Refs. 12-17). Giger et al. (Ref. 12) developed an automated detection scheme based on multiple gray-level thresholding and geometric feature analysis. Armato et al. (Refs. 13-16) extended the method to include a three-dimensional approach and linear discriminant analysis.
A major problem with certain known CAD schemes for lung nodule detection is a relatively large number of false positives, which cause difficulty in the clinical application of the CAD scheme. A large number of false positives are likely to disturb the radiologist""s task in lung nodule detection and interpretation, thus lowering the efficiency of the radiologist""s task with the CAD scheme. In addition, radiologists may lose their confidence in using the CAD scheme. Therefore, it is very important to reduce the number of false positives as much as possible, while maintaining a high sensitivity.
A database used in a study discussed throughout this specification included 38 non-infused, low-dose thoracic helical CT (LDCT) scans acquired from 31 different patients who participated voluntarily in a lung cancer screening program between 1996 and 1998 in Nagano, Japan (Refs. 3, 18, 7). The CT examinations were performed on a mobile CT scanner (CT-W950SR; Hitachi Medical, Tokyo, Japan). The scans used for this study were acquired with a low-dose protocol of 120 kVp, 25 mA (11) or 50 mA (27 scans), 10-mm collimation, and a 10-mm reconstruction interval at a helical pitch of two (Ref 18). The pixel size was 0.586 mm for 33 scans and 0.684 mm for five scans. Each reconstructed CT section had an image matrix size of 512xc3x97512 pixels. The 38 scans consisted of 1057 sections, and included 38 xe2x80x9cmissedxe2x80x9d nodules that represent biopsy-confirmed lung cancers and were not reported during the initial clinical interpretation (Ref. 7).
Technical details of a known scheme have been published previously in Refs 13-16, in which lung nodule identification proceeds in three phases: two-dimensional (2D) processing, followed by three-dimensional (3D) analysis, and then the application of classifiers. A gray-level thresholding technique is applied to a 2D section of a CT scan for automated lung segmentation. Modifications to the resulting lung segmentation regions are made by use of a rolling-ball technique (Refs. 19, 8) that eliminates the trachea and main-stem bronchi when they are erroneously included within the lung regions.
A multiple gray-level-thresholding technique is applied to the segmented lung volume. Individual structures are identified by grouping of spatially contiguous pixels that remain in the volume at each of 36 gray-level thresholds. Because a nodule is defined radiologically as any well-demarcated, soft-tissue focal opacity with a diameter less than 3 cm (Ref. 20), a structure is identified as a nodule candidate if the volume of the structure is less than that of a 3-cm-diameter sphere.
The categorization of nodule candidates as xe2x80x9cnodulexe2x80x9d or xe2x80x9cnon-nodulexe2x80x9d is based on a combination of a rule-based classifier and a series of two linear discriminant classifiers applied to a set of nine 2D and 3D features extracted from each nodule candidate. The features are 3D gray-level-based features, 3D morphological features, and 2D morphological features: (1) the mean gray level of the candidate, (2) the gray-level standard deviation, (3) the gray-level threshold at which the candidate was identified, (4) volume, (5) sphericity, (6) radius of the sphere of equivalent volume, (7) eccentricity, (8) circularity, and (9) compactness.
In this CAD scheme, the multiple gray-level-thresholding technique initially identified 20,743 nodule candidates in 1057 sections of LDCT images (Ref 7). Then a rule-based classifier followed by a series of two linear discriminant classifiers was applied for removal of some false positives, thus yielding a detection of 41 (82.0%) of 50 nodules together with 1,078 (28.4 per case and 1.02 per section) false positives (Ref. 21). In this study, all 50 nodules and all 1078 false positives were used; the 1078 false positives included in this evaluation were considered as xe2x80x9cvery difficultxe2x80x9d false positives.
Recently, in the field of signal processing, nonlinear filters based on a multilayer artificial neural network (ANN), called neural filters, have been studied. In the neural filter, the multilayer ANN is employed as a convolution kernel. The neural filters can acquire the functions of various linear and nonlinear filters through training. It has been demonstrated that the neural filters can represent an averaging filter, weighted averaging filters, weighted median filters, morphological filters, microstatistic filters, generalized-weighted-order statistical filters, an epsilon filter, and generalized stack filters (Refs. 22-25). In the applications of the neural filters to reduction of the quantum mottle in X-ray fluoroscopic and radiographic images, it has been reported that the performance of the neural filter was superior to that of the nonlinear filters utilized in medical systems and a well-known nonlinear filter (Refs. 26-32). The performance of the neural filter was superior to that of the conventional nonlinear filters.
On the other hand, in the field of computer vision, a supervised edge detector based on a multilayer ANN, called a neural edge detector, has been developed (Refs. 33-36). The neural edge detector can acquire the function of a desired edge detector through training. It has been reported that the performance of the neural edge detector on edge detection from noisy images was far greater than that of the conventional edge detectors such as the Canny edge detector, the Marr-Hildreth edge detector, and the Huckel edge detector (Refs. 33, 34). In its application to the contour extraction of the left ventricular cavity in digital angiography, it has been reported that the neural edge detector can accurately detect the subjective edges traced by cardiologists (Refs. 35, 36).
First, the invention provides a method of training an artificial neural network including network parameters that govern how the artificial neural network operates, the method having the steps of receiving at least a likelihood distribution map as a teacher image; receiving at least a training image; moving a local window across plural sub-regions of the training image to obtain respective sub-region pixel sets; inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides output pixel values; comparing the output pixel values to corresponding teacher image pixel values to determine an error; and training the network parameters of the artificial neural network to reduce the error.
Second, the invention provides a method of detecting a target structure in an image by using an artificial neural network, the method having the steps of scanning a local window across sub-regions of the image by moving the local window for each sub-region, so as to obtain respective sub-region pixel sets; inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides, corresponding to the sub-regions, respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map; and scoring the likelihood distribution map to detect the target structure.
Third, the invention provides an apparatus for detecting a target structure in an image, the apparatus having a network configured to receive sub-region pixel sets from respective sub-regions of the image, and to operate on the sub-region pixel sets so as to produce a likelihood distribution map including output pixel values that represent likelihoods that corresponding image pixels are part of the target structure.
Fourth, the invention provides a method for detecting a target structure in an image, the method having the steps of training first through N-th artificial neural networks, N being an integer greater than 1, on either (A) a same target structure and first through N-th mutually different non-target structures, or (B) a same non-target structure and first through N-th mutually different target structures, the first through N-th artificial neural networks being configured to output first through N-th respective indications of whether the image includes a target structure or a non-target structure; and combining the first through N-th indications to form a combined indication of whether the image includes a target structure or a non-target structure.
Fifth, the invention provides an apparatus for detecting a target structure in an image, the apparatus having first through N-th artificial neural networks, N being an integer greater than 1, that have been trained on either (A) a same target structure and first through N-th mutually different non-target structures, or (B) a same non-target structure and first through N-th mutually different target structures, the first through N-th artificial neural networks being configured to output first through N-th respective indications of whether the image includes a target structure or a non-target structure; and a combiner configured to combine the first through N-th indications to form a combined indication of whether the medical image includes a target structure or a non-target structure.
The invention further provides various combinations of the foregoing methods and apparatus.
The invention further provides computer program products storing program instructions for execution on computer systems, which when executed by the computer systems, cause the computer system to perform the inventive method steps.
In particular embodiments and applications of the present invention to which the scope of the claims should not be limited, none, one or more of the following may apply:
the image may be a medical image;
the target structure may be an abnormality in the medical image;
the non-target structures may be normal anatomical structures in the medical image;
the network may be configured to receive sub-region pixel sets from respective consecutively physically overlapping sub-regions of the medical image that are displaced by a predetermined distance;
the predetermined distance may be a pixel pitch value in the medical image, so that successive sub-regions are offset from each other by a separation distance of adjacent pixels in the medical image; and/or
the artificial neural network provides the respective output pixel values that represent the likelihoods that the respective medical image pixels are part of an abnormality.
Other objects, features and advantages of the invention will become apparent to those skilled in the art when reading the following Detailed Description with reference to the accompanying drawings.